22 years ago, Indian-American engineer Omar Syed watched the Deep Blue vs. Garry Kasparov match, and had an idea. Humans didn’t need to learn how to beat computers at chess—they needed a new game that they would always be better than computers at. It should be simple enough for humans to learn, yet complex enough that computers would not be able to “brute-force” their way to victory by considering every possible combination of moves, as Deep Blue had.

Arimaa is playable with the same board and pieces as chess, although it’s more fun to play with a custom Arimaa set. Here’s what you need to know:

All pieces have animal names. Instead of pawns, knights, bishops, rooks, queens, and kings, Arimaa has rabbits, cats, dogs, horses, camels, and elephants, in that order.

All pieces move the same way. You get four “moves” each turn, and each move consists of moving a piece to an adjacent space. You can split those moves between pieces any way you like.

You don’t capture enemy pieces by landing on them. Instead, you have to get them onto a “trap”—one of four special spaces clustered around the middle of the board—and then isolate them from any adjacent friendly pieces.

Larger animal pieces outrank smaller animal pieces. For instance, horses outrank cats, dogs, and rabbits, but not elephants, camels, or other horses. Elephants outrank all other pieces but themselves, while rabbits don’t outrank any pieces. Outranking an adjacent enemy piece confers three benefits:

When your turn begins in a game of chess, you will have, on average, about 35 different legal moves to choose from. You want to choose the move that gives you the biggest advantage, and you determine that by anticipating which of the 35 or so legal moves your opponent is likely to make, which in turn will influence your next move, which will influence their next move, and so on. Each move you add to your calculations increases their complexity by a factor of 35 or so. This is referred to as chess’s “branching factor.” Branching factor is not terribly important for human players, as humans are excellent at weeding out obviously poor moves and focusing on the few potentially good ones. But it is for computers.

Because chess’s branching factor is a relatively low 35, Deep Blue was able to consider all possible board positions of moves further in advance than Kasparov, giving it more insights into which move should be played in the present. This was how it won.

Go, the East Asian stone-laying game, has a branching factor of about 250. Arimaa, however, has an average branching factor of about 17,000! If the number of calculations a computer has to do goes up by a factor of 17,000 for each additional move the computer considers, the computer can only “think” a few moves in advance—theoretically giving humans the advantage.

But with the advent of neural networks and machine learning, computers are learning to avoid wasting time considering poor moves, freeing them up to think deeper about the good ones. This has enabled them to beat humans at games with high branching factors.

In March 2016, a Google program defeated 18-time Go champion Lee Sedol four games to one. And in April 2015, the program bot_Sharp defeated three top human Arimaa players seven games to two. The game supposed to be hard for computers is now easy for computers.

But chess and go are both ancient games centuries old. Arimaa is only as old as the Class of 2020, and new strategies and tactics are being discovered all the time. If you feel like chess is dead—if you feel like there are no new things about the game to discover—you’re not alone. Join me.